The 3rd  Workshop on

Rough Sets and Emerging Intelligent Systems Paradigms


Cairo, Egypt, Monday 13 August,  2007

Organized by:

Egyptian Rough Sets Working Group


Faculty of Computer and Information, Cairo University



Keynote Speaker


Data Uncertainty: Reasons and Queries


Dr.Mohamed F. Mokbel


Data uncertainty is ubiquitous in many applications as it could be inherent from erroneous data entries or inaccuracy in device readings. Examples of inaccurate devices include sensor readings and location-detection devices (e.g., GPS-like devices and RFIDs). Data uncertainly could be also desired and embedded into accurate data as a means for privacy-preserving data management. Data uncertainty does not only affect data storing issues, but also it poses new challenges to query processors. Existing query processors are designed to deal with certain data with no direct extension to tolerate uncertainty. In this talk, we will discuss two aspects of data uncertainty. First, we will discuss various sources of data imperfection and uncertainty. Second, we will discuss various techniques that tune existing query processors to tolerate data uncertainly.


Mohamed F. Mokbel (Ph.D., Purdue University, 2005, MS, B.Sc., Alexandria University, 1999, 1996) is an assistant professor in the Department of Computer Science and Engineering, University of Minnesota. His main research interests focus on advancing the state of the art in the design and implementation of database engines to cope with the requirements of emerging applications (e.g.,location-based applications and sensor networks). Mohamed is also interested in indexing, adaptive query processors, object-based storage devices, and geographic information systems. Mohamed has joined Lawrence Livermore National Lab, Microsoft Research, and Hong Kong Polytechnic University at summers 2002, 2004, and 2006, respectively. He is an ACM and IEEE member. For more information,
please visit


We are delighted to invite you to participate in our 3rd workshop on Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP), on 13 August  2007.

Conference Objectives:


   Rough set theory, proposed by Zdzislaw Pawlak (1926-2006) in 1982, is a model of approximate reasoning. The main idea is based on the indiscernibility relation that describes indistinguishability of objects. Concepts are represented by lower and upper approximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, data mining, machine learning, finance, industry, multimedia, medicine, and most recently bioinformatics. 


The main objective of this workshop  is to provide a forum for mathematician, engineers, academicians, scientists and researchers to present the result of their research activities in the field of rough sets and their applications. The primary focus of the workshop is to create an effective medium for institutions and students to share ideas, innovations and problem solving techniques. On this basis, the Egyptian Rough Sets working group with the faculty of Computer and Information,  Cairo University is calling for papers to be submitted to the 3rd  workshop on Rough Sets and Emerging Intelligent Systems Paradigms which  addressing theoretical, empirical and policy issues related to this theme, we would appreciate to receive  your paper  by 15 June, 2007.



Important information:

  •   The paper  must not exceed 4 pages in MS-word format

  •   For ERS member and nun-member (open)

  •   The paper  will published as a technical report published (on-line/hardcopy) by the ERS.

  •   The presentation time is 20  minuts including the disusssion

  •   Award will given to  the Best Student Presentation and to the Best Student paper.

  •  A certificate for each accepted paper

Workshop Place



Faculty of Computers and Information, Cairo University,
5 Ahmed Zoweil St., Dokki, Giza, Egypt



Papers on these and related subjects are particularly encouraged:

  • Rough set theory and applications

  • Fuzzy set theory and applications

  • Fuzzy-rough, rough-fuzzy and beyond

  • Knowledge discovery and data mining

The conference''s focus will also be on the following topics:

  • approximate reasoning

  • computational biology

  • data warehousing

  • decision support systems

  • distributed computing

  • evolutionary strategies

  • formal concept analysis

  • human-computer interaction

  • layered learning

  • machine intelligence

  • bioinformatics

  • multi-agent systems

  • multimedia mining

  • non standard logics

  • pattern recognition

  • semantic web and ontologies

  • web and text mining



Important date


                                 Submissions due:   15  June,     2007






Authors should submit the electronic version of their papers in MS-WORD  formats by email to



Workshop Committee

Honorary Chair

Professor James F. Peters

Department of Electrical and Computer Engineering

Room E2-390 Engineering Building

University of Manitoba

75A Chancellor's Circle

Winnipeg, MB R3T5 V 6CANADA

Tel: 204-474-9603,  Fax:204 -261-4639


Co-Editor-in-Chief:  Springer Transactions on Rough Sets

Workshop General Chair

Professor  Aly Fahmy,

Faculty of Computers and Information
Cairo University,
5 Ahmed Zoweil St., Dokki
Giza, Egypt
Tel: (+202) 3350107  - : (+2012) 3420162
Fax: (+202) 3350109
Personal e-mail:

Workshop Coordinators and Co-Chair

Professor  Reem Bahgat,

Vice Dean of Research and Higher Studies, 

 Faculty of Computers and Information, Cairo University


Dr.  Aboul Ella Hassanien,  ERS coordinator

Workshop Program Committee

·         Dr. Mohamed Mohamed Ezzat Abdel-Monsef Mohamed, Tanta University

·         Dr. Amgad Salama Salem, Tanta University

·         Professor  A.M Kozae, Tanta University,

·         Prof. Abdel-Badeeh M. Salem, Ain Shams University

·         Dr. Yasser Fouad Mahmoud Hassan, Alexandria  University

·         Dr. Hala Shawky Own, NRIAG, Helwan

·         Dr. Nahla El-haggar, NRIAG,  Helwan

·         Dr. Wael Abd El-Kader Awad Suez Canal University

·         Prof.Dr. Farahat Farag Farhat,Sadat Academy

·         Dr Hussam Elbehiery,Egyptian Armed Forces Research Center

·         Dr. Tarek Gharib Fouad, Ain Shams University.

·         Professor Mahmoud Mohamed Hassan Gabr, Alexandria University



Submission and Guideline for authors


Please submit your paper in MS word format  directly the   Dr. Aboul Ella   via


The guide line to write your paper. [Guideline]




For further Information, please contact:



Submitted papers

  • M. Sammany, T. Medhat " Rough Set and Neural Networks for Data Classification"

  • Marwa Sharawi, Mohammed Sammany, Mohammed El- Beltagy,  Iman Saroit " Intrusion Detection Scheme using Neural Networks"

  • A. M. Kozae, T. Medhat, R. Mareay " Topological Spaces and Covering Rough set"

  • Hala Own "Theorem proving prediction based rough set theory”

  • Aboul Ella Hassanien, Rough Sets Theory in its application in Image Processing

  • S. EL-Assar and E-Ghareeb Bi-Double Stone Algebras and Rough sets

  • Mohamed A. Tahoun1, Mohammed A-Megeed  "Improving the Performance of CBIR Systems via Multiple Features Representations"


Bachelor Student Projects Sections

The organizing committees of the 3rd workshop will open a session for Bachelor students in Egypt to present their graduate projects  during the workshop. So, we invites students to submit  one page summary of their graduated projects by 15 June 2007.

  1. We will rearward some of them to presents their project in 10 min in the workshop regular session

  2. Award will given to  the Best Student project. ( 200 L.E)

  3. We will marketing the  project and  host it on the workshop web site

 So, if you are  interest, please send me one page summary + Short biography of the student + personal photo

  • Students around the world are welcome to apply



Visakh C R, Praveen T P

Amrita School of Engineering, Coimbatore, Tamil Nadu, India


Hybrid vehicles combine the technology of both Internal Combustion (IC) engine and electric engine in such a way as to take advantage of the benefits provided by these power sources while compensating for each others short comings resulting in highly efficient driving performance. A major controlling factor is the efficient torque split between the IC engine and the electric motor. To achieve this, a fuzzy logic based controller has been developed in this project. The controller takes into account the accelerator pedal position and battery state of charge as the controlling factors. Membership functions were developed and proper if-then rules were written. The working of the controller was checked using optimum values generated using Advisor. Assembly code for Motorola microcontrollers was generated using FUDGE. A naïve support vector machine was developed utilizing the if-then conditions written for fuzzy logic controller. The developed machine was able to predict like the fuzzy system


Computer Aided Molecular Design using Artificial Immune System


 Ali Mamdouh Al Kahki,    Amr Kourany Ali,    Hazem Ahmed Saleh,     Mahmoud Yahia Mahmoud and Amr Ahmed Badr

Cairo University, faculty of computer and information


In the last decade, Computational intelligence CI approaches have been increasingly used in the drug discovery process. Many algorithms were proposed to design new drugs or predict the activity of drugs that have never been synthesized “virtual screening”. In this project we used new computational intelligence approach which is Artificial Immune System ( AIS ) and applied it in De-novo drug  design with some modifications in the originally proposed work to find novel drugs for a given antigen . A molecular visualization tool was developed to render the generated molecules with different rendering models. Also, we designed a new algorithm in the field of Quantitative Structure-Activity Relationship (QSAR) to predict the biological activity of drugs using AIS and genetic algorithm GA and also it was used in the  features selection phase along side with a special type of neural network called Generalized Regression Neural Network GRNN for model building, the final result was compared the results obtained from AIS and GA.In this work we experienced many techniques in complex design problems using evolutionary algorithms and in manipulating and optimizing 3D molecules structures and many tools in data analysis like principle component analysis PCA and other statistical tools.  Results of this work show that AIS is a promising approach in CI which gave good results in De-novo drug design and exceeded the GA in QSAR features selection. Also, GRNN gives a better prediction than many previous methods and it was more efficient than traditional feed forward neural network.